Method and apparatus for reduction of metal artifacts in ct images

ABSTRACT

A method and apparatus include acquisition of a view dataset based on x-rays received by a detector corresponding to a energy level, reconstruction of an initial image using the view dataset, the initial image comprising a plurality of metal voxels at respective metal voxel locations, and generation of a metal mask corresponding to the plurality of metal voxels within the initial image. The method and apparatus also include forward projection of the metal mask onto the view dataset to identify metal dexels in the view dataset, performance of a weighted interpolation based on the identified metal dexels to generate a completed view dataset, reconstruction of a final image using the completed view dataset, the final image comprising a plurality of image voxels corresponding to the metal voxel locations, and replacement of a portion of the plurality of image voxels corresponding to the metal voxel locations with smoothed metal values.

BACKGROUND OF THE INVENTION

Embodiments of the invention relate generally to diagnostic imaging and,more particularly, to a method and apparatus for reduction of metalartifacts in CT images.

Typically, in computed tomography (CT) imaging systems, an x-ray sourceemits a fan or cone-shaped beam toward a subject or object, such as apatient or a piece of luggage. Hereinafter, the terms “subject” and“object” shall include anything capable of being imaged. The beam, afterbeing attenuated by the subject, impinges upon an array of radiationdetectors. The intensity of the attenuated beam radiation received atthe detector array is typically dependent upon the attenuation of thex-ray beam by the subject. Each detector element of the detector arrayproduces a separate electrical signal indicative of the attenuated beamreceived by each detector element. The electrical signals aretransmitted to a data processing system for analysis which ultimatelyproduces an image.

Generally, the x-ray source and the detector array are rotated about thegantry within an imaging plane and around the subject. X-ray sourcestypically include x-ray tubes, which emit the x-ray beam at a focalpoint. X-ray detectors typically include a collimator for collimatingx-ray beams received at the detector, a scintillator for convertingx-rays to light energy adjacent the collimator, and photodiodes forreceiving the light energy from the adjacent scintillator and producingelectrical signals therefrom. Typically, each scintillator of ascintillator array converts x-rays to light energy. Each scintillatordischarges light energy to a photodiode adjacent thereto. Eachphotodiode detects the light energy and generates a correspondingelectrical signal. The outputs of the photodiodes are then transmittedto the data processing system for image reconstruction.

Generally, in the absence of object scatter, a system derives behaviorat a different energy based on a signal from two relative regions ofphoton energy from the spectrum: the low-energy and the high-energyportions of the incident x-ray spectrum. In a given energy regionrelevant to medical CT, two physical processes dominate the x-rayattenuation: (1) Compton scatter and the (2) photoelectric effect. Thedetected signals from two energy regions provide sufficient informationto resolve the energy dependence of the material being imaged.Furthermore, detected signals from the two energy regions providesufficient information to determine the relative composition of anobject composed of two hypothetical materials, or the effective atomicnumber distribution with the scanned object.

Techniques to obtain energy sensitive measurements comprise: (1) scanwith two distinctive energy spectra and (2) detect photon energyaccording to energy deposition in the detector. Such measurementsprovide energy discrimination and material characterization, and may beused to generate reconstructed images using a base materialdecomposition (BMD) algorithm. A conventional BMD algorithm is based onthe concept that, in an energy region for medical CT, the x-rayattenuation of any given material can be represented by a proper densitymix of two materials with distinct x-ray attenuation properties,referred to as the base or basis materials. The BMD algorithm computestwo CT images that represent the equivalent density of one of the basematerials based on the measured projections at high and low x-ray photonenergy spectra, respectively.

A principle objective of energy sensitive scanning is to obtaindiagnostic CT images that enhance information (contrast separation,material specificity, etc.) within the image by utilizing two or morescans at different chromatic energy states. A number of techniques havebeen proposed to achieve energy sensitive scanning including acquiringtwo or more scans either (1) back-to-back sequentially in time where thescans require multiple rotations of the gantry around the subject or (2)interleaved as a function of the rotation angle requiring one rotationaround the subject, in which the tube operates at, for instance, 80 kVpand 140 kVp potentials.

High frequency generators have made it possible to switch the kVppotential of the high frequency electromagnetic energy projection sourceon alternating views. As a result, data for two or more energy sensitivescans may be obtained in a temporally interleaved fashion rather thantwo separate scans made several seconds apart as typically occurs withprevious CT technology. The interleaved projection data may furthermorebe registered so that the same path lengths are defined at each energyusing, for example, some form of interpolation.

However, it is known that objects with high x-ray absorption properties(e.g., metal) can cause artifacts in reconstructed CT images, oftenresulting in images having low- or non-diagnostic image quality. Forexample, metal implants such as amalgam dental fillings, jointreplacements (i.e., plates and/or pins used in hips, knees, shoulders,etc.), surgical clips, or other hardware may generate streak orstarburst artifacts in the formation of such images. Such artifactstypically result from the sharp difference in signal attenuation at theboundary of the metal implants and a patient's anatomy.

Many correction techniques or methods are known for reducing or alteringsuch artifact streaks. For example, one known technique for reducingartifact streaks includes the use of iterative image reconstructionalgorithms with weighting designed to ameliorate the metal artifacts.However, full iterative reconstruction is not widely used clinically.

Another technique for correcting metal artifacts includes performing“projection completion” whereby a corrupted portion of a projection is“completed” or replaced with synthetic projection data having morefavorable properties. A shortcoming of projection completion methods isrobustness in terms of differing reconstruction parameters (e.g., fieldsof view), acquisition protocols (e.g., axial or helical), method ofmetal segmentation and projection interpolation, metal replacement, andthe like. Furthermore, projection completion techniques are not directlyapplicable to multi-energy imaging.

Therefore, it would be desirable to develop an apparatus and method toreduce metal artifacts that provides consistent results usingnon-iterative reconstruction frameworks. Also, that addresses theabove-described short-comings of projection completion methods.Furthermore, it would be desirable to design an apparatus and method forreducing metal artifacts in CT images applicable to multi-energyimaging.

BRIEF DESCRIPTION OF THE INVENTION

Embodiments of the invention are directed to a method and apparatus forreduction of metal artifacts in CT images.

Therefore, in accordance with one aspect of the invention, a computerreadable storage medium has stored thereon a computer program comprisinginstructions, which, when executed by a computer, cause the computer toacquire a first view dataset based on x-rays received by a detectorcorresponding to a first energy level, reconstruct an initial imageusing the first view dataset, the initial image comprising a pluralityof metal voxels at respective metal voxel locations, and generate ametal mask corresponding to the plurality of metal voxels within theinitial image. The instructions also cause the computer to forwardproject the metal mask onto the first view dataset to identify metaldexels in the first view dataset, perform a weighted interpolation basedon the identified metal dexels to generate a completed first viewdataset, reconstruct a first final image using the completed first viewdataset, the first final image comprising a plurality of image voxelscorresponding to the metal voxel locations, and replace a portion of theplurality of image voxels corresponding to the metal voxel locationswith smoothed metal values.

In accordance with another aspect of the invention, a method includesreconstructing an image using an imaging dataset based on x-raysreceived by a detector corresponding to a first energy level, generatinga metal mask from the image corresponding to metal voxel locationswithin the imaging dataset, and forward projecting the metal mask ontothe imaging dataset. The method also includes identifying a plurality ofmetal dexels and a plurality of non-metal dexels within the imagingdataset based on the metal mask, removing the plurality of metal dexelsfrom the imaging dataset, and generating a plurality of interpolateddexels via a weighted interpolation algorithm. The method furtherincludes replacing each of the plurality of metal dexels in the imagingdataset with a respective interpolated dexel of the plurality ofinterpolated dexels, reconstructing a final image using the plurality ofnon-metal dexels and the plurality of interpolated dexels, and smoothingimage voxels in the final image corresponding to the metal voxellocations.

In accordance with another aspect of the invention, an imaging systemincludes a rotatable gantry having an opening for receiving an object tobe scanned, and an x-ray source coupled to the gantry and configured toproject x-rays through the opening. The imaging system also includes agenerator configured to energize the x-ray source to a first energylevel to generate x-rays corresponding to the first energy level, adetector having pixels therein, the detector attached to the gantry andpositioned to receive x-rays projected from the x-ray source, and acomputer. The computer is programmed to access a first projectiondataset corresponding to the first energy level, reconstruct a firstimage from the first projection dataset, and segment the first image toidentify metal locations. The computer is also programmed to forwardproject the segmentation onto the first projection dataset to identify aplurality of metal detector pixels in the first projection dataset,remove the plurality of metal detector pixels from the first projectiondataset, and use a weighted interpolation to replace the removedplurality of metal detector pixels and to complete the first projectiondataset. Further, the computer is programmed to reconstruct a completedfirst image using the completed first projection dataset and smoothportions of the completed first image corresponding to the metallocations of the first energy image.

Various other features and advantages will be made apparent from thefollowing detailed description and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate preferred embodiments presently contemplated forcarrying out the invention.

In the drawings:

FIG. 1 is a pictorial view of a CT imaging system.

FIG. 2 is a block schematic diagram of the system illustrated in FIG. 1.

FIG. 3 is a perspective view of one embodiment of a CT system detectorarray.

FIG. 4 is a perspective view of one embodiment of a detector.

FIG. 5 is a flowchart illustrating a technique for reducing metalartifacts according to embodiments of the invention.

FIG. 6 is a flowchart illustrating a technique for reducing metalartifacts according to embodiments of the invention.

FIG. 7 is a pictorial view of a CT system for use with a non-invasivepackage inspection system according to an embodiment of the invention.

DETAILED DESCRIPTION

The operating environment of embodiments of the invention is describedwith respect to a sixty-four-slice computed tomography (CT) system.However, it will be appreciated by those skilled in the art thatembodiments of the invention are equally applicable for use with othermulti-slice configurations. Moreover, embodiments of the invention willbe described with respect to the detection and conversion of x-rays.However, one skilled in the art will further appreciate that embodimentsof the invention are equally applicable for the detection and conversionof other high frequency electromagnetic energy. Embodiments of theinvention will be described with respect to a “third generation” CTscanner, but is equally applicable with other CT systems as well asvascular and surgical C-arm systems and other x-ray tomography systems.

Referring to FIG. 1, a computed tomography (CT) imaging system 10 isshown as including a gantry 12 representative of a “third generation” CTscanner. Gantry 12 has an x-ray source 14 that projects a beam of x-raystoward a detector assembly or collimator 18 on the opposite side of thegantry 12. Referring now to FIG. 2, detector assembly 18 is formed by aplurality of detectors 20 and data acquisition systems (DAS) 32. Theplurality of detectors 20 sense the projected x-rays 16 that passthrough a medical patient 22, and DAS 32 converts the data to digitalsignals for subsequent processing. Each detector 20 produces an analogelectrical signal that represents the intensity of an impinging x-raybeam and hence the attenuated beam as it passes through the patient 22.During a scan to acquire x-ray projection data, gantry 12 and thecomponents mounted thereon rotate about a center of rotation 24.

Rotation of gantry 12 and the operation of x-ray source 14 are governedby a control mechanism 26 of CT system 10. Control mechanism 26 includesan x-ray controller 28 that provides power and timing signals to anx-ray source 14 and a gantry motor controller 30 that controls therotational speed and position of gantry 12. An image reconstructor 34receives sampled and digitized x-ray data from DAS 32 and performs highspeed reconstruction. The reconstructed image is applied as an input toa computer 36 which stores the image in a mass storage device 38.

Computer 36 also receives commands and scanning parameters from anoperator via console 40 that has some form of operator interface, suchas a keyboard, mouse, voice activated controller, or any other suitableinput apparatus. An associated display 42 allows the operator to observethe reconstructed image and other data from computer 36. Theoperator-supplied commands and parameters are used by computer 36 toprovide control signals and information to DAS 32, x-ray controller 28and gantry motor controller 30. In addition, computer 36 operates atable motor controller 44 which controls a motorized table 46 toposition patient 22 and gantry 12. Particularly, table 46 moves patients22 through a gantry opening 48 of FIG. 1 in whole or in part.

As shown in FIG. 3, detector assembly 18 includes rails 17 havingcollimating blades or plates 19 placed therebetween. Plates 19 arepositioned to collimate x-rays 16 before such beams impinge upon, forinstance, detector 20 of FIG. 4 positioned on detector assembly 18. Inone embodiment, detector assembly 18 includes fifty-seven detectors 20,each detector 20 having an array size of 64×16 of pixel elements 50. Asa result, detector assembly 18 has sixty-four rows and nine hundredtwelve columns (16×57 detectors) which allows sixty-four simultaneousslices of data to be collected with each rotation of gantry 12.

Referring to FIG. 4, detector 20 includes DAS 32, with each detector 20including a number of detector elements 50 arranged in pack 51.Detectors 20 include pins 52 positioned within pack 51 relative todetector elements 50. Pack 51 is positioned on a backlit diode array 53having a plurality of diodes 59. Backlit diode array 53 is in turnpositioned on multi-layer substrate 54. Spacers 55 are positioned onmulti-layer substrate 54. Detector elements 50 are optically coupled tobacklit diode array 53, and backlit diode array 53 is in turnelectrically coupled to multi-layer substrate 54. Flex circuits 56 areattached to face 57 of multi-layer substrate 54 and to DAS 32. Detectors20 are positioned within detector assembly 18 by use of pins 52.

In the operation of one embodiment, x-rays impinging within detectorelements 50 generate photons which traverse pack 51, thereby generatingan analog signal which is detected on a diode within backlit diode array53. The analog signal generated is carried through multi-layer substrate54, through flex circuits 56, to DAS 32 wherein the analog signal isconverted to a digital signal.

Referring now to FIG. 5, a metal artifact reduction technique 60 is setforth according to an embodiment of the invention. As discussed indetail below, technique 60 acquires or accesses an initial projectiondataset or initial view dataset and performs a standard reconstructionof the initial projection dataset to generate an initial reconstructedCT image dataset. A metal mask is generated by first applying athreshold to the reconstructed image dataset and subsequently optionallyapplying morphological operations to modify the mask. The metal mask isused to indicate locations of metal voxels within the image dataset. Themetal mask is forward projected onto the projection data to identifydetector pixels containing metal, or dexels, in the initial viewdataset. All detectors cells marked as being impacted by the metal maskare completed via a weighted three-dimensional interpolation. Anoptional smoothing is applied to the interpolated patch before replacingthe metal dexels in the initial view dataset with the completed patch.The completed projection data is reconstructed to generate a final CTimage dataset. Because former metal voxels may be improperly representedin the final CT image dataset, the metal voxels are replaced in thefinal CT image dataset with modified and smoothed image voxelscorresponding to the metal voxel locations. In a multi-energy context,the projection interpolation, interpolation patch smoothing, and metalreplacement may be applied in a joint fashion to preserve the physicalmeaning of the BMD images.

Technique 60 begins by accessing an initial projection dataset at step62. The initial projection dataset may be accessed from a storagelocation or from a live or real-time scan. At step 64, technique 60performs a standard reconstruction of projection data to generate aninitial reconstructed CT image dataset. According to embodiments of theinvention, one or multiple sets of images may be reconstructed from anyof high-energy images, low-energy images, monochromatic images (at anykeV level), images blended in any combination of high and low energyimages, and material basis images or material decomposed images (of anymaterial basis combination).

The initial CT image dataset includes a number of reconstructed imageslices corresponding to parameter set “A.” According to one embodiment,the initial reconstruction is performed with centered targeting and afull display field of view (e.g., 500 mm) to increase the probability ofcapturing metal in the reconstructed volume. That is, the field of viewand target center for the initial reconstruction may differ from thefield of view and target center for the final reconstruction, which willbe reconstructed with parameter set “B.” Also, any fixed reconstructionkernel may be used so the metal mask does not depend on a user-specifiedreconstruction kernel. Thus, the generated metal mask will not beaffected if, for example, the user changes the reconstruction kernel.For cases including a tilted gantry, the initial reconstruction may alsobe performed assuming a gantry tilt value of zero so that the forwardprojector does not need to account for gantry tilt. Other embodimentsmay account for gantry tilt directly in the forward projector.

For helical scanning applications, images are reconstructed at the samez locations as the original request, according to one embodiment.Alternatively, additional z slices, or z slices with different spacingmay be reconstructed to increase the probability of fully covering anymetal objects or for the purpose of yielding a more refined metal mask.

For axial scanning applications, additional slices may be reconstructedoutside of the standard image volume range or primary reconstructionvolume in a direction orthogonal to the axial scan plane (i.e., in thez-direction). The additional slices are reconstructed to acquirenon-primary reconstruction data and determine the location of metaloutside the primary reconstruction region that may still be incidentupon the detector during the acquisition. Specifically, x-ray paths withlarger cone angles may pass through areas of an object that are notcovered by the standard image volume. If the x-ray path that propagatesthrough metal is not in the standard reconstruction volume, but theprojection of the metal is still present in the views, the dexels maynot be correctly identified by the forward projection procedure. Theseunlabeled dexels may potentially be identified as valid values duringprojection completion, thereby re-introducing metal into the completedview and causing artifacts. According to one embodiment, sixty-fourslices may be reconstructed for a 32 slice scan, and ninety-six slicesmay be reconstructed for a 64 slice scan. However, one skilled in theart will recognize that any number of additional slices may bereconstructed based on clinical experience. In one embodiment,corresponding view-weighting parameters are applied to the additionalslices. The view-weighting parameter is based on the image z-offset fromthe central scan plane and is symmetric about the scan plane. However,one skilled in the art will recognize that different values ofview-weighting can be used based on clinical experience.

At step 66, technique 60 segments each image of the initial CT imagedataset using a threshold value to indicate metal voxels within theimage dataset. As used herein, the term “metal” is used to denoteobjects or voxels in the image corresponding to high x-ray attenuationproperties even if those objects are not metal. The segmentationgenerates a binary metal mask by applying the threshold to thereconstructed image dataset and inserting any voxels above the thresholdvalue into the metal mask.

Optionally, the binary metal mask may be further processed usingdilations and erosions in order to remove small pieces of metal from themask and slightly expand the metal region, thus preventing partialvoluming effects. An erosion operation removes a voxel from the metalmask if greater than T_(e) of its neighboring voxels are not in themetal mask. A dilation operation inserts a voxel into the metal mask ifgreater than T_(d) of its neighbors are in the metal mask. According toone embodiment, N_(e) erosion iterations are applied to the metal mask,followed by N_(d) dilation iterations. By applying the erosions first,some or all small metal features are eliminated from the metal maskunder the assumption that x-rays have penetrated a small object and thusthere is no photon starvation ‘behind’ the small piece of metal.Following the erosion process, the first N_(e) dilations reinsert mostof the original metal mask voxels into the updated metal mask. However,the small regions or metal features eliminated by the erosions are notreintroduced. The remaining N_(d)−N_(e) dilations expand the metal maskregion and fill in holes. N_(d) may be larger or smaller than N_(e).Furthermore, N_(d) and N_(e) may assume any integer value. This optionalprocedure of further processing using dilations and erosions helps toprevent streak artifacts resulting from not including metal voxels fromthe initial reconstructed CT image dataset in the metal mask during theinitial segmentation.

The neighborhood of a voxel for the erosion and dilation operations maybe defined in a variety of ways. According to embodiments of theinvention, these neighborhoods can be defined as any combination of setsof 2D or 3D neighbors. For example, according to one embodiment,erosions are performed among the eight nearest in-plane neighbors (i.e.,a 2D neighborhood), and dilations are performed among the twenty-sixnearest 3D neighbors (i.e., a 3D neighborhood).

The values of T_(e) and T_(d) may be functions of the neighborhooddefinitions. In one embodiment, both T_(e) and T_(d) are zero so that anerosion operation removes a voxel from the metal mask if any of itsneighbors are not in the metal mask and a dilation operation inserts avoxel into the metal mask if any of its neighbors are in the metal mask.

At step 68, the metal mask is forward projected onto the initialprojection dataset. Specifically, the metal voxels, which may beindicated by non-zero values in the binary metal mask, are projectedonto the detector for a given source position. Those detector pixels ordexels that have a non-zero contribution from the forward projection arelabeled as metal detector pixels or metal dexels.

According to one embodiment, in a dual energy imaging procedure,technique 60 assumes that the image datasets corresponding to eachenergy level have projections at an identical set of view angles. Thatis, technique 60 assumes the two datasets are registered with oneanother. Thus, the same metal mask is forward projected onto each viewangle of both energy datasets.

According to one embodiment, the forward projection of step 68 isperformed with a pixel-driven approach that accounts for the “spread” ofa given voxel onto the detector for a given source position. First, thedetector coordinate intersection for the ray connecting the sourceposition and the voxel center is calculated, similar to pixel-drivenback projection. This detector coordinate intersection point may bedefined as u in units of detector channels. However, instead of labelingonly the four nearest dexels using an analogue to bilinearinterpolation-based backprojection, the spread calculation correctlylabels the full range of dexels impacted by this metal voxel as metaldexels. Otherwise, a metal dexel may be potentially skipped over becausethe dexel is not one of the nearest four dexels to the detectorintersection point u. That is, if two consecutive voxels are marked asmetal in the metal mask, projecting them to the detector using abilinear interpolation approach may leave an unlabeled dexel between twopairs of dexels labeled from the voxel projections even though thelabeled metal dexels should be contiguous.

In one embodiment, the spread may be calculated based upon amagnification factor, mag, applied to the most orthogonal axis (x or y)of the current voxel from the perspective of the ray connecting thesource and voxel center. The magnification factor can be defined as thedistance from the source to the current voxel center divided by thedistance from the source to the detector. The angle θ is determinedbetween the source and current voxel center and the positive y-axis. Thespread s of the current metal voxel on the detector is then given by:

$\begin{matrix}{s = \left\{ \begin{matrix}{{{{mag} \cdot \Delta}\; {x \cdot {{\cos (\theta)}}}}:} & {{{\cos (\theta)}} > {{\sin (\theta)}}} \\{{{{mag} \cdot \Delta}\; {y \cdot {{\sin (\theta)}}}}:} & {{otherwise},}\end{matrix} \right.} & \left( {{Eqn}.\mspace{14mu} 1} \right)\end{matrix}$

where ΔAx and Δy define the width and height of the voxels,respectively. The detector channel range is then given by [u_(min),u_(max)] where u_(min)=u−s/(2Δu), u_(max)=u+s/(2Δu), and Δu is the widthof a detector channel.

The detector coordinate range in the z-direction is also calculated, byfirst computing the detector row intersection point, ν, in units ofdetector rows for the ray connecting the source and the voxel center.The row spread is then defined by the range [ν_(min), ν_(max)] wheredetector height, Δν, the z projection of the voxel center onto thedetector is given by:

ν_(min)=ν−0.5DΔz/(dΔν)

ν_(max)=ν+0.5DΔz/(dΔν)  (Eqn. 2),

where Δz is the non-negative slice spacing, Δν is the height of thedetector row, d is the in-plane source to voxel center distance (i.e.,the distance in the x and y directions, but not the z direction), and Dis the source to detector distance. While these calculations may assumea specific system geometry (e.g., a detector with no curvature in z),one skilled in the art could adapt the calculations for a differentgeometry.

According to another embodiment, the forward projection of step 68projects the eight voxel corners onto the detector to yield eightdetector intersection points. The values u_(min) and u_(max) are thenthe minimum and maximum detector channel intersection points,respectively, in the set of eight intersection points. Similarly, theν_(min) and ν_(max) values are then minimum and maximum detector rowintersection points, respectively.

Finally, all dexels in the calculated range are labeled as metal dexels.Specifically, a dexel with channel c_(i) and row r_(j) is marked as ametal dexel if └u_(min)┘≦c_(i)≦└u_(max)┘ and └ν_(min)┘≦r_(j)≦└ν_(max)┘where └x┘ is the floor operation.

Some forward projected rays may extend outside of the availablereconstructed volume in the z direction prior to intersecting thedetector. As noted above with respect to the generation of additionalimage slices for the axial case, unidentified metal in the z directionmay cause problems during the projection interpolation. In oneembodiment, these problems may be addressed by padding the image volumesuch that the edge slices are replicated, extrapolated from existingslices, or based upon prior knowledge, including anatomical knowledge ora scout scan. In another embodiment, the forward projector may accountfor this directly. Specifically, if a metal voxel in an edge slice ofthe image volume forward projects onto the detector, then ν_(min) orν_(max) is extended to the edge of the detector in the same direction aswould be used if the edge slice were replicated.

At step 70 (shown in phantom), technique 60 applies optionalpre-smoothing to the projection data to reduce streaking. In oneembodiment, pre-smoothing can be applied as a weighted average of agiven neighborhood of a dexel that will be used during theinterpolation. The neighborhood may be defined in one, two, or threedimensions and the weights may depend on the distances to the neighbors,the total number of available neighbors, and/or the amount of smoothingdesired. Thus, the pre-smoothing takes the form:

$\begin{matrix}{{{\hat{d}}_{i,j,k} = {\sum\limits_{{\{{i^{\prime},j^{\prime},k^{\prime}}\}} \in N_{i,j,k}}{\left( \frac{\alpha \left( {i^{\prime},j^{\prime},k^{\prime}} \right)}{\sum\limits_{{\{{i^{''},j^{''},k^{''}}\}} \in N_{i,j,k}}{\alpha \left( {i^{\prime},j^{\prime},k^{\prime}} \right)}} \right)d_{i^{\prime},j^{\prime},k^{\prime}}}}},} & \left( {{Eqn}.\mspace{14mu} 3} \right)\end{matrix}$

where N_(i,j,k) is the set of valid (i.e., non-metal or previouslycompleted) voxels in a neighborhood of voxel (i,j,k) and α is a weightthat depends on the voxel (i′, j′, k′). In the case that no validneighbors are available, the pre-smoothing has no effect.

Technique 60 completes or replaces all detectors cells marked as beingimpacted by the metal mask via a weighted interpolation at step 72. Thecompletion step interpolates in row, channel, and view directions usingvalid neighbors of a metal dexel that are not metal dexels, assignsweights to each of the neighbors, and replaces the metal dexel by thesum of the weighted neighbors. In addition, previously completedprojection data can be used when interpolating in the view direction.For dual kVp imaging, technique 60 applies identical interpolationcoefficients to replace corresponding elements of views acquired orsynthesized at multiple energies.

In one embodiment, the interpolation used to replace the metal dexeld_(i,j,k) given by channel i, row j, and view k takes the form:

{tilde over (d)} _(i,j,k) =W _(c) C+W _(R) R+W _(V) V  (Eqn. 4),

where W_(c), W_(R), and W_(v) represent weighting values in the channel,row, and view direction, respectively. Furthermore, C, R, and Vrepresent contributions from the channel, row, and view directions,where C, R, and V can depend on any of the available projection data,including previously completed values.

A binary mask, U, is defined such that U_(i,j,k) has a value of one ifdexel d_(i,j,k) is a metal dexel and zero otherwise. Further, thenearest neighbors of metal voxel d_(i,j,k) that are not metal dexels inthe channel, row, and view directions are defined as i_(i), i₂, j₁, j₂,k₁, k₂, respectively. For example, i₁ may be defined as the largestindex such that i₁<i and U_(i1,j,k)=0. Similarly, i₂ may be defined asthe smallest index such that i₂>i and U_(i2,j,k)=0. Neighbor indices forthe row and view directions (i.e., j₁, j₂, k₁, and k₂) may be defined ina similar manner. Some values may be undefined if there are no non-metaldexels in a certain direction (e.g., if off the edge of the detector inthe channel and row case or if all dexels in a given direction are metaldexels). Furthermore, maximum distances can be defined in the row,channel, and view directions so that neighbors are undefined if thenearest valid neighbor would exceed the maximum distance for thatdirection. Other embodiments may include additional neighbors in eachdirection for use with higher-order interpolation strategies.

With respect to the historical view data d_(i,j,k1), for all views afterthe first view, the dexel (i,j) in the previous view is either not ametal dexel or has already been completed. Thus k₁ is k−1 except for thefirst view where k₁ is undefined. However, the age of dexel (i, j)(i.e., the number of views since a given dexel was not a metal dexel)can also be used for weighting during the interpolation. Otherembodiments may use the last non-metal historical dexel directly ratherthan the potentially previously completed dexel from the prior view.

Interpolated values for channel (I_(c)), row (I_(r)), and view (I_(ν))are defined as follows:

I _(c)=α₁₁ {tilde over (d)} _(i1,j,k)+α₁₂ {tilde over (d)} _(i2,j,k)

I _(r)=α₂₁ {tilde over (d)} _(i,j1,k)+α₂₂ {tilde over (d)} _(i,j2,k)

I _(ν)=α₃₁ {tilde over (d)} _(i,j,k1)+α₃₂ {tilde over (d)}_(i,j,k2)  (Eqn. 5).

According to one embodiment,

${\alpha_{11} = {1 - \alpha_{12}}},{\alpha_{12} = \frac{i - i_{1}}{{i_{2} - i_{1}},}}$${\alpha_{21} = {1 - \alpha_{22}}},{a_{22} = \frac{j - j_{1}}{j_{2} - j_{1}}},{{{and}\mspace{14mu} \alpha_{31}} = {1 - \alpha_{32}}},{\alpha_{32} = \frac{k_{2} - k}{A_{i,j,k} + k_{2} - k}},{where}$

A_(i,j,k) is the age of dexel (ij) in view k. Ages may be initialized toan arbitrary value, for example, A_(init)=100, for use before a validdexel is available. Other embodiments may include higher-orderinterpolation if additional neighbors are available in a givendirection, different interpolation coefficients, or alternate historicalview weighting strategies.

The interpolated values I_(c),I_(r),I_(ν) apply where two neighborsexist in a given direction from which to interpolate. If only a singleneighbor is available in a single direction, that single neighbor valueis used for that direction. If no neighbors are available in a givendirection, that direction is not used in the interpolation. Theinterpolated values I_(c),I_(r),I_(ν) depend on dexel indices i,j,k.However, the indices are assumed fixed here forth for purposes ofexplanation.

The interpolated values in each direction are combined according toweighting functions, W, based on a distance, D, calculated for thatdirection. The distance function may differ for the cases when one ortwo valid neighbors is available. Specifically, distances are definedby:

D _(O)(ν₁ ,E,ν)=|ν−ν|√{square root over (|E−ν ₁|)}

D _(T)(ν₁,ν₂,ν)=min({circumflex over (ν)}₁, {circumflex over(ν)}₂)√{square root over (max({circumflex over (ν)}₁,{circumflex over(ν)}₂))}where {circumflex over (ν)}₁=min(|ν₁−ν|,CLIP) and {circumflexover (ν)}₂=min(|ν₂−ν|,CLIP)  (Eqn. 6),

where α, β, and λ denote scaling factors to adjust the relative weightsof the row, channel, and view directions, D_(T) is a two-sided distance(i.e., involving two neighbors), D_(O) is a one-sided distance (i.e.,involving one valid neighbor), E indicates an ‘edge’ value on theopposite side of the one valid value (i.e., E is −1 or one past the lastpossible row, channel, or view), and CLIP is a clipping threshold. Thesedistance calculations account for the fact that the previousinterpolations in the given directions will be more accurate in the casewhen one of the distances is small, while also not too heavilypenalizing the case that only one of the distances is large. Thedistances in the channel, row, and view direction may then be composedusing these distance functions. For example, assuming the two-sideddistance function is applicable, the channel, row, and view distancefunctions are given by:

D _(c) =αD _(T)(i ₁ ,i ₂ ,i)

D _(r) =βD _(T)(j ₂ ,j ₁ ,j)

D _(ν) =λD _(T)(−A _(i,j,k) ,k ₂ −k,0)  (Eqn. 7),

where α, β, and λ denote scaling factors to adjust the relative weightsof the row, channel, and view directions. For other embodiments,different distance definitions may be used. The weightings α, β, and λcan be used where data is expected to be more reliable in some directiondue, for example, to anatomical knowledge, reconstruction parameters,a-priori information such as a scout scan, or known clinical scenarios.

With these distance metrics defined, according to one embodiment, theweighting functions, W, are given as:

$\begin{matrix}{{{W_{C}\left( {C,R,V} \right)} = \frac{\left( {1/C} \right)}{\left( {1/C} \right) + \left( {1/R} \right) + \left( {1/V} \right)}}{{W_{R}\left( {C,R,V} \right)} = \frac{\left( {1/R} \right)}{\left( {1/C} \right) + \left( {1/R} \right) + \left( {1/V} \right)}}{{W_{V}\left( {C,R,V} \right)} = \frac{\left( {1/V} \right)}{\left( {1/C} \right) + \left( {1/R} \right) + \left( {1/V} \right)}}} & \left( {{Eqn}.\mspace{14mu} 8} \right)\end{matrix}$

where C, R, and V are distance metrics computed in the channel, row, andview directions. Each of C, R, and V may be based on a two-sideddistance, a one-sided distance, or not be available. In cases where thevalues are not available due to the lack of valid neighbors, theweighting equations are adjusted accordingly.

After generating the completion mask, technique 60 optionally smoothesthe completion mask at step 74 (shown in phantom) to prevent steaks inthe resulting images due to sharp changes in value from one dexel to thenext. Only metal dexels are smoothed at step 74. The completionsmoothing applies zero or more convolutions for each of the completedmetal dexels. In one embodiment, the convolution is 2D (i.e., in channeland row, but not in view), but a 3D convolution can also be used tosmooth in view. The convolution coefficients can be tuned and selectedbased on clinical experience, anatomical regions, reconstructionparameters, or a-priori information such as a scout scan. Although someof the neighboring dexels may be metal dexels, all metal dexels havebeen completed and are thus valid for use in smoothing.

According to one embodiment, smoothing is carried out in the row andchannel directions. For example, dexels {d_(i±1, j±1,k)} may be used tosmooth dexel d_(i,j,k). If dexel d_(i,j,k) is on an edge of thedetector, some neighboring dexels may not be defined. If such is thecase, dexel d_(i,j,k) is replaced with:

$\begin{matrix}{{d_{i,j,k}^{\prime} = {{c \cdot d_{i,j,k}} + {\sum\limits_{m = 1}^{m = {N}}{N_{m}\frac{1 - c}{N}}}}},} & \left( {{Eqn}.\mspace{14mu} 9} \right)\end{matrix}$

where N is the set of all defined neighbors, |N| is the number ofdefined neighbors, N_(m) is the m-th neighbor, and c is the weightassigned to dexel d_(i,j,k). Alternatively, smoothing could be carriedout in all three directions and with differently defined neighborhoodsand smoothing coefficients.

According to one embodiment, the completion smoothing may be appliediteratively, by providing the result of the prior smoothing iteration asan input. Alternatively, the completion smoothing may be omitted.

At step 76, technique 60 reconstructs the completed projection data togenerate a final CT image dataset. The image voxels of the final CTimage dataset are now marked in the final CT image dataset because thereconstructed values are not correct. Specifically, former metal voxelsmay appear washed out in the final CT image dataset or be incorrectlyscaled in the case of material basis images.

According to one embodiment, technique 60 replaces the values of imagevoxels that are former metal voxels in a set of material basis images,such as, for example, water and iodine images. The former metal voxelsare replaced such that the resulting monoenergetic images behave asexpected for some assumed material (e.g., titanium) when the materialbasis images are combined. That is, as the monoenergetic energyincreases, the attenuation of the material decreases in accordance withan assumed attenuation curve of the material. The method of choosing thevoxels in the image dataset reconstructed in step 76 may vary. In oneembodiment, a threshold is applied to the calculated fractions, f_(k),to generate a binary metal mask for the final reconstructions. In thisembodiment, a threshold, t, is defined for the fractional metal coveragevalues above which a voxel is considered to be metal and will bereplaced.

To replace the former metal voxels in the material basis images,technique 60 performs a least squares fit between the per-keVattenuation coefficient calculated from the material basis images andthe expected values for the assumed metal material. For example,technique 60 may perform a least squares fit between water-iodine ratiosand titanium-water ratios over a range of energies to provide water andiodine replacement values that behave as expected for titanium over arelevant keV range. The least squares fit can be weighted to enforce acloser match at some specified keV, such as, for example, 70 keV. Theresulting weighted values can then be scaled such that the monoenergeticvalue at the specified keV matches the original metal threshold value orsome other expected or desired value. This weighting and scalingprocedure generates a constant value that is used to replace the metalvoxels from the initial reconstruction. For a single energy case orsingle energy images from a multi-energy case, on the other hand,technique 60 may use a metal threshold or a constant value to replacethe metal voxels. In either case, lookup table or an adaptive thresholdbased on information determined within the scan may be used to createthe replacement value.

Because the final reconstruction parameter set “B” may differ from theinitial reconstruction parameter set “A” (in field of view, targetcentering, slice spacing, and gantry tilt, for example), technique 60re-samples the metal mask generated from the initial CT image dataset tothe geometry of the final CT image dataset at step 78.

In one embodiment, at step 78, technique 60 calculates the fraction ofthe volume of each voxel in the final reconstruction that is “covered”by metal voxels in the original metal mask. The calculated fractionsindicate how much of each newly reconstructed voxel was labeled as metalin the initial reconstruction.

The total volumetric intersection between the metal voxels in the firstreconstruction and the k^(th) voxel in the final reconstruction isdefined as:

$\begin{matrix}{{v_{k} = {\sum\limits_{i \in N^{\prime}}{{{x_{i}^{\prime}\bigcap x_{k}}} \cdot {{y_{i}^{\prime}\bigcap y_{k}}} \cdot {{z_{i}^{\prime}\bigcap z_{k}}}}}},} & \left( {{Eqn}.\mspace{14mu} 10} \right)\end{matrix}$

where x_(k) denotes the range of x values covered by voxel k, a∩bdenotes the intersection of two ranges, |x_(k)| denotes the length of aninterval, N′ is the set of indices of non-zero metal mask voxels in thefirst reconstruction and x′, y′, and z′ are coordinates in the firstreconstruction. Similar definitions hold for y_(k), |y_(k)|, z_(k), and|z_(k)|. Thus, the fraction, f_(k), of the k^(th) voxel that is coveredby metal in the new reconstruction is:

$\begin{matrix}{f_{k} = {\frac{v_{k}}{{x_{k}} \cdot {y_{k}} \cdot {z_{k}}}.}} & \left( {{Eqn}.\mspace{14mu} 11} \right)\end{matrix}$

The calculated fractions, f_(k), are used to determine metal replacementvalues, as described below. According to one embodiment, technique 60re-samples the original metal mask and not the mask generated afterapplying morphological operations to the original. Another embodimentreapplies the morphological operations to the initial metal mask, butwith different neighborhoods. For example, with initial neighborhoods of2D and 3D for the erosions and dilations, respectively, the erosions anddilations can both be reapplied to the original mask with 2Dneighborhoods and the result used for the mask re-sampling step.

For cases including gantry tilt, according to embodiments of theinvention where the initial reconstruction was performed with theassumption of no gantry tilt, re-sampling can take into account the factthat the y positioning of the z slices may differ between the initialand final reconstructions. In alternative embodiments, the initialreconstruction may take into account the gantry tilt.

Technique 60 smoothes the replaced metal voxels in the final CT imagedataset with smoothed metal values at step 80. According to oneembodiment, technique 60 applies a smoothing function based on thefraction of a given neighborhood around the voxel to be replaced thatincludes metal. The set of voxels to be replaced within a givenneighborhood of the voxel to be replaced are defined as D_(c). Then thefraction of the neighborhood covered by metal is calculated as:

$\begin{matrix}{{F_{c} = \frac{\sum\limits_{k \in D_{c}}f_{k}}{D_{c}}},} & \left( {{Eqn}.\mspace{14mu} 12} \right)\end{matrix}$

where |D_(c)| is the volume or area of the neighborhood and f_(k) isgiven above. Further the set of voxels that will not be replaced, D_(n),is given as:

D _(n) ={k∈D _(c) |f _(k) <t}  (Eqn. 13)

for some threshold t.

The mean, u, of the reconstructed values, I_(k), represented by voxelindices in D_(n) is calculated. If every voxel in the neighborhood willbe replaced by metal (i.e., D_(n)is empty) then the mean, u, is set to ametal replacement value, m, as described above.

Technique 60 interpolates between u and m using an interpolationcoefficient based on F_(c). According to one embodiment, F_(c) ^(n) isused as the interpolation coefficient to control the relativecontribution from the values u and m. The initially reconstructed value,I_(k), is replaced with a smoothed replacement value, I′_(k) accordingto:

I′ _(k)=(1−F _(c) ^(n))μ+F _(c) ^(n) m  (Eqn. 14)

for some value of n.

According to one embodiment, the neighborhood that determines D_(c) isdefined based on a radial distance. In particular, voxels whose centerslie within some distance r of the center of the voxel to be replaced areincluded in D_(c). This radial distance can be applied in-plane (i.e, adisc in 2D) or volumetrically (i.e., a sphere in 3D). Other embodimentscould include different (e.g., non-radial) neighborhoods.

For the dual energy imaging case, technique 60 may jointly apply theabove-described smoothing operation on both basis images near the edgesof the replaced metal region to smooth the appearance of the replacementmask. According to one embodiment, identical weighting is applied toboth material basis images so that the smoothed weighted region behavesas expected when performing subsequent basis image combinations. Afterthe replaced metal voxels in the final CT image dataset are smoothed,technique 60 may form new images as weighted combinations of low andhigh kV energy images or material decomposed images.

According to another embodiment of the invention, replacement of metalvoxels may be performed directly in images formed by weightedcombinations of a given set of available energy and material basisimages. Further, the weighted smoothing operation may be applieddirectly to the metal mask in the weighted combination images.

Embodiments of the interpolation technique described with respect toFIG. 5 may use fixed directions with distance-based weighting.Alternatively, an initial image with metal removed may be forwardprojected to yield per-dexel estimates of the expected projectionvalues, as described in detail below with respect to FIG. 6. Theseforward projected values may then be used to guide the interpolation byproviding additional information. For example, the forward projectionmay indicate that a metal dexel is expected to be most similar tonon-metal dexels along a different direction than the fixedinterpolation. In that case, performing the interpolation in thedirection indicated by the forward projection should improve the qualityof the final image. Metal voxels corresponding to metal locationsidentified in the CT image dataset may be replaced with nearby non-metalvoxels (e.g., voxels corresponding to tissue or water locations) or aconstant (e.g., water) prior to performing forward projection. Bysynthesizing values in the metal replacement region prior to forwardprojection, the completion of the metal regions in the projectiondataset may be carried out via interpolation in any direction. Theforward projection may be performed using any of the energy levelimages, material basis images, or monoenergetic images of a multi-energysystem. Alternatively, the forward projection may be performed using asingle energy (kVp) image in a conventional scanning system. Thissynthesizing procedure may aid in recovering edge information andprevent streaking that would otherwise be introduced in the finalreconstructed image. The synthesizing procedure may also be appliedafter a number of iterations of a fixed-direction interpolationprocedure, such as that described with respect to technique 60 of FIG.5.

Additionally, in a dual energy imaging procedure, a low energy and ahigh energy image may be used to find residual artifacts and replacethose values prior to forward projection. Typically, the difference inattenuation values for a given anatomy between high and low energyimages falls within a given range. For example, water has approximatelythe same attenuation value in both high and low energy images, so thedifference between the two images is approximately zero. The differencebetween high and low energy images for other anatomy falls within knownranges. In images with severe metal artifacts, the difference valuesbetween the high and low energy images are outside a reasonableanatomical range. Thus, by comparing high and low energy images, metalvoxels corresponding to artifacts may be identified. These identifiedmetal voxels may be replaced either via the synthesizing proceduredescribed above or with a constant (e.g., water) prior to forwardprojection to prevent metal artifacts from corrupting the forwardprojected values.

Referring now to FIG. 6, technique 82 may be applied to reduce metalartifacts in CT images, according to embodiments of the invention. Inone embodiment, technique 82 may be applied as an alternative totechnique 60 (FIG. 5). In another embodiment, technique 82 may beapplied as a second pass (or one of multiple passes) after an initialapplication of technique 60.

Technique 82 begins by accessing an initial projection dataset at step84. The initial projection dataset may be accessed from a storagelocation or in real-time during a scan. At step 86, an initial imagedataset is reconstructed to generate an initial reconstruction volume.In one embodiment, an initial image dataset is reconstructed asdescribed in step 64 of technique 60 (FIG. 5). In another embodiment, aninitial image dataset is reconstructed by applying steps 62-76 oftechnique 60. Thus, the initial reconstruction volume may contain imagesfrom one of one or more energies, basis material decomposition images,or any combination of energy images and BMD images, with some or all ofsteps 62-76 of technique 60 applied to reduce metal artifacts in theinitial image dataset.

Referring back to FIG. 6, after reconstructing the initial image datasetat step 86, technique 82 generates a metal mask at step 88. According toone embodiment, technique 82 generates the metal mask in a similarmanner as described in step 66 of technique 60 (FIG. 5). Alternatively,if technique 60 was applied to generate the initial reconstruction,technique 82 may reuse the metal mask generated at step 66 of technique60.

At step 90 (shown in phantom), technique 82 applies an optionalmodification to the images reconstructed during step 86. These originalimages may or may not include metal artifacts, depending on whether ornot technique 60 of FIG. 5 was applied to generate the original imagedata set in step 86.

According to one embodiment, during step 90 the voxels identified by themetal mask generated in step 88 are replaced in the image domain toprevent a subsequent forward projection step from including metal in theforward projected data. The metal mask voxels may be replaced by aconstant, such as a value to indicate water or soft tissue in oneembodiment. Alternatively, the metal mask voxels may be replaced by an“image completion” process, which replaces the metal voxels byinterpolations among the nearby non-metal voxels, similar to theprojection domain interpolation described in step 72 (FIG. 5). Inaddition to interpolating nearby voxels, a constant value may also beincorporated into the interpolation. For example, the interpolation mayperform a completion by considering both the weighted combination ofnearby non-metal voxels and a constant such as water or soft tissue.

In another embodiment, technique 82 may additionally replace voxels thatwere not included in the metal mask. For example, if datasetscorresponding to two energies are available, then computing thedifference between one image volume and the other may indicate thelocation of severe reconstruction artifacts that should be corrected.Any voxel whose absolute value of the computed difference exceeds areasonable range for expected patient anatomy or object properties mayalso be replaced by the image completion of step 90, as described above.The reasonable range may be fixed, may be determined based on priorknowledge (i.e., reconstruction parameters or a priori knowledge of theanatomy to be scanned), or may be determined through informationobtained from a scout scan, according to embodiments of the invention.Other embodiments may apply additional morphological operations to themetal mask. For example, the metal mask may be further dilated to removevoxels that may be corrupted but are seemingly not metal. Thesepotentially corrupt voxels are replaced, but are not considered metalvoxels for the subsequent steps.

Technique 82 applies a binary forward projection operation at step 92 onthe metal mask generated at step 88, such as the binary forwardprojection described with respect to step 68 of technique 60 (FIG. 5).As a result of applying step 92, a mask of dexels is generated “behind”the metal voxels that will be replaced during a subsequent operation.One skilled in the art will recognize that other forward projectionprocedures may also be used to generate a mask of metal dexels from theavailable metal image mask.

At step 94, technique 82 applies a full forward projection to the imagevolume resulting from step 90. Unlike the binary forward projection ofstep 92 that simply determines the dexels that have been affected bymetal, the full forward projection of step 94 calculates an estimatedvalue for a given dexel at a given view angle by forward projecting thevolume from step 90.

According to one embodiment, the full forward projection of step 94 iscomputed using “Siddon's method,” which calculates the line intersectionlengths between a projection ray and each voxel and then computes theline integral of that projection ray by summing the products of thevoxel values and their associated line intersection lengths. Thoseskilled in the art will recognize that other projection methods can alsobe used to estimate the projection data given the image volume from step90.

At optional step 96 (shown in phantom), the projection data may bepre-smoothed, such as described with respect to step 70 of technique 60(FIG. 5), where {circumflex over (d)}_(i,j,k) represents thepre-smoothed projection data at index (i,j,k).

Returning to FIG. 6, at step 98, technique 82 uses the estimatesgenerated by the forward projection in step 94 to guide an interpolationprocess that is used to replace the dexels indicated by the binaryforward projection of step 92. Unlike the fixed projection interpolationdescribed in step 72 of technique 60 (FIG. 5), the forwardprojection-guided interpolation of step 98 may adaptively determine thedirections along which it will interpolate. According to embodiments ofthe invention, step 98 may use a portion of the forward projectionvalues computed by step 94. Thus, step 94 may restrict the forwardprojection to the projection rays that will be used for the forwardprojection-guided interpolation at step 98.

According to one embodiment, step 98 may calculate a distance metricbetween the forward projected value for a dexel to be replaced,d_(i,j,k) ^(F), and all of the voxels along the “periphery” of thereplacement region. The periphery of the replacement region is aneighborhood defined about that replacement region. For example, oneembodiment may include all dexels adjacent to metal dexels in theperiphery, where adjacency is defined as two dexels whose row andchannel indices are within one row and/or channel of each another. Thepreviously completed value at dexel (i,j,k−1) may also be considered anon-metal dexel and included in the periphery. The peripheral dexels maythen be sorted by their distance metric to dexel (i,j,k) and some subsetof the lowest distance peripheral dexels may then be chosen to definethe direction along which interpolation will occur. The set of dexelindices defining the periphery of the replacement region may be definedas P and the set of voxels among which the interpolation will occur maybe defined as

{d_(i_(n)^(′), j_(n)^(′), k_(n)^(′))^(F)}_(n = 1)^(N),

(i′, j′,k′)∈P. Rather than using the values

{d_(i_(n)^(′), j_(n)^(′), k_(n)^(′))^(F)}_(n = 1)^(N)

directly, the real projection data with the same indices is used duringthe interpolation.

Technique 82 applies a weighted forward projection guided interpolationat step 100 to generate a completion patch. In one embodiment, theforward projection guided interpolation takes the form:

$\begin{matrix}{{d_{i,j,k}^{new} = {{\alpha_{i,j,k}d_{i,j,k}^{F}} + {\sum\limits_{n = 1}^{N}{\beta_{n}{\hat{d}}_{i_{n}^{\prime},j_{n}^{\prime},k_{n}^{\prime}}}}}},} & \left( {{Eqn}.\mspace{14mu} 15} \right)\end{matrix}$

where α_(i,j,k) may be chosen to define the influence of the estimatedvalue on the final value and the β_(n) weights may be chosen based onthe calculated distance metrics. The β_(n) weights may additionallydepend on the dexel indices (i,j,k) so that the weighted interpolationcoefficients may be normalized such that

${\alpha_{i,j,k} + {\sum\limits_{n = 1}^{N}\beta_{n}}} = 1.$

The α_(i,j,k) may be adjusted on a dexel-by-dexel basis based on theexpected accuracy of the forward projected value at dexel index (i,j,k).For example, the difference between d_(i,j,k) ^(F) and the correspondingcompletion value calculated may be computed by applying step 72 oftechnique 60 (FIG. 5) and setting α_(i,j,k) as a function of thisdifference. In this fashion, the contribution of d_(i,j,k) ^(F) may bedecreased by decreasing α_(i, j, k) as the difference increases byassuming that the forward projected value k may not be accurate due tocorruption in the image domain.

Referring back to FIG. 6, after calculating the completion patch at step100, technique 82 optionally smoothes the completion patch at step 102(shown in phantom), similar to the smoothing described with respect tostep 74 of technique 60 (FIG. 5).

Technique 82 of FIG. 6 reconstructs the now-completed projection data atstep 104 to generate a metal artifact reduced CT image volume inaccordance with parameter set “B,” similar to step 76 of technique 60(FIG. 5). At step 106 of FIG. 6, technique 82 re-samples the metal maskfrom parameter set “A” to parameter set “B,” similar to step 78 oftechnique 60 (FIG. 5). As shown in FIG. 6, at step 108, technique 82applies metal replacement in the newly reconstructed CT image volume ina similar fashion as step 80 of technique 60.

Referring now to FIG. 7, package/baggage inspection system 110 includesa rotatable gantry 112 having an opening 114 therein through whichpackages or pieces of baggage may pass. The rotatable gantry 112 housesa high frequency electromagnetic energy source 116 as well as a detectorassembly 118 having scintillator arrays comprised of scintillator cellssimilar to that shown in FIG. 3 or 4. A conveyor system 120 is alsoprovided and includes a conveyor belt 122 supported by structure 124 toautomatically and continuously pass packages or baggage pieces 126through opening 114 to be scanned. Objects 126 are fed through opening114 by conveyor belt 122, imaging data is then acquired, and theconveyor belt 122 removes the packages 126 from opening 114 in acontrolled and continuous manner. As a result, postal inspectors,baggage handlers, and other security personnel may non-invasivelyinspect the contents of packages 126 for explosives, knives, guns,contraband, etc.

Embodiments of the invention have been described with respect to singleand dual energy CT imaging. However, one skilled in the art willrecognize that embodiments of the invention are equally applicable totriple energy CT imaging procedures, for example. Embodiments of theinvention are also equally applicable to projection-based BMD andimage-based BMD. Further, embodiments of the invention have beendescribed with respect to integrating detectors. However, one skilled inthe art will recognize that embodiments of the invention may similarlybe used for system employing photon counting detectors. Further,embodiments of the invention described herein are equally applicable toother types of tomographic imaging such as CT attenuation correctionimages for single photon emission computed tomography (SPECT) orpositron emission tomography (PET), three-dimensional x-ray imaging,vascular and surgical C-arm systems, radiation therapy planningscanners, other tomographic x-ray systems, and the like.

A technical contribution for the disclosed method and apparatus is thatit provides for a computer implemented method and apparatus forreduction of metal artifacts in CT images.

Therefore, in accordance with one embodiment, a computer readablestorage medium has stored thereon a computer program comprisinginstructions, which, when executed by a computer, cause the computer toacquire a first view dataset based on x-rays received by a detectorcorresponding to a first energy level, reconstruct an initial imageusing the first view dataset, the initial image comprising a pluralityof metal voxels at respective metal voxel locations, and generate ametal mask corresponding to the plurality of metal voxels within theinitial image. The instructions also cause the computer to forwardproject the metal mask onto the first view dataset to identify metaldexels in the first view dataset, perform a weighted interpolation basedon the identified metal dexels to generate a completed first viewdataset, reconstruct a first final image using the completed first viewdataset, the first final image comprising a plurality of image voxelscorresponding to the metal voxel locations, and replace a portion of theplurality of image voxels corresponding to the metal voxel locationswith smoothed metal values.

In accordance with another embodiment, a method includes reconstructingan image using an imaging dataset based on x-rays received by a detectorcorresponding to a first energy level, generating a metal mask from theimage corresponding to metal voxel locations within the imaging dataset,and forward projecting the metal mask onto the imaging dataset. Themethod also includes identifying a plurality of metal dexels and aplurality of non-metal dexels within the imaging dataset based on themetal mask, removing the plurality of metal dexels from the imagingdataset, and generating a plurality of interpolated dexels via aweighted interpolation algorithm. The method further includes replacingeach of the plurality of metal dexels in the imaging dataset with arespective interpolated dexel of the plurality of interpolated dexels,reconstructing a final image using the plurality of non-metal dexels andthe plurality of interpolated dexels, and smoothing image voxels in thefinal image corresponding to the metal voxel locations.

In accordance with yet another embodiment, an imaging system includes arotatable gantry having an opening for receiving an object to bescanned, and an x-ray source coupled to the gantry and configured toproject x-rays through the opening. The imaging system also includes agenerator configured to energize the x-ray source to a first energylevel to generate x-rays corresponding to the first energy level, adetector having pixels therein, the detector attached to the gantry andpositioned to receive x-rays projected from the x-ray source, and acomputer. The computer is programmed to access a first projectiondataset corresponding to the first energy level, reconstruct a firstimage from the first projection dataset, and segment the first image toidentify metal locations. The computer is also programmed to forwardproject the segmentation onto the first projection dataset to identify aplurality of metal detector pixels in the first projection dataset,remove the plurality of metal detector pixels from the first projectiondataset, and use a weighted interpolation to replace the removedplurality of metal detector pixels and to complete the first projectiondataset. Further, the computer is programmed to reconstruct a completedfirst image using the completed first projection dataset and smoothportions of the completed first image corresponding to the metallocations of the first energy image.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

1. A computer readable storage medium having stored thereon a computerprogram comprising instructions, which, when executed by a computer,cause the computer to: acquire a first view dataset based on x-raysreceived by a detector corresponding to a first energy level;reconstruct an initial image using the first view dataset, the initialimage comprising a plurality of metal voxels at respective metal voxellocations; generate a metal mask corresponding to the plurality of metalvoxels within the initial image; forward project the metal mask onto thefirst view dataset to identify metal dexels in the first view dataset;perform a weighted interpolation based on the identified metal dexels togenerate a completed first view dataset; reconstruct a first final imageusing the completed first view dataset, the first final image comprisinga plurality of image voxels corresponding to the metal voxel locations;and replace a portion of the plurality of image voxels corresponding tothe metal voxel locations with smoothed metal values.
 2. The computerreadable storage medium of claim 1 wherein the instructions cause thecomputer to generate the metal mask by applying a metal threshold to theinitial image.
 3. The computer readable storage medium of claim 2wherein the instructions further cause the computer to perform dilationand erosion operations on the metal mask.
 4. The computer readablestorage medium of claim 1 wherein the instructions further cause thecomputer to replace the plurality of metal voxels in the reconstructedinitial image with at least one of a constant value and interpolatedvalues corresponding to nearby non-metal voxels.
 5. The computerreadable storage medium of claim 1 wherein the instructions furthercause the computer to apply a full forward projection to thereconstructed initial image.
 6. The computer readable storage medium ofclaim 1 wherein the instructions further cause the computer to apply aforward projection-guided interpolation to generate a completion patchcorresponding to the metal dexel locations.
 7. The computer readablestorage medium of claim 1 wherein the instructions further cause thecomputer to forward project the metal mask onto the first view datasetbased on a spread of a respective metal voxel onto the detector.
 8. Thecomputer readable storage medium of claim 1 wherein the instructionscause the computer to perform a weighted three-dimensional interpolationusing row, channel, and view data in a neighborhood of respective metaldexels of the first view dataset being interpolated.
 9. The computerreadable storage medium of claim 1 wherein the instructions cause thecomputer to perform the weighted interpolation using historicalcompleted data.
 10. The computer readable storage medium of claim 1wherein the instructions that cause the computer to acquire a first viewdataset cause the computer to acquire the first view dataset in an axialscanning mode; and wherein the instructions that cause the computer toreconstruct an initial image cause the computer to: reconstruct a firstplurality of image slices corresponding to a primary reconstructionvolume for an axial scan; and reconstruct a second plurality of imageslices corresponding to a non-primary reconstruction volume in adirection orthogonal to an axial scan plane of the first view dataset,the non-primary reconstruction volume outside the primary reconstructionvolume.
 11. The computer readable storage medium of claim 1 wherein theinstructions further cause the computer to: acquire a second viewdataset based on x-rays received by the detector corresponding to asecond energy level; forward project the metal mask onto the second viewdataset to identify metal dexels in the second view dataset; perform theweighted interpolation based on the metal dexels identified in thesecond view dataset to generate a completed second view dataset; andreconstruct a second final image, the second final image comprising aplurality of image voxels corresponding to the metal voxel locations.12. The computer readable storage medium of claim 11 wherein theinstructions that cause the computer to perform the weightedinterpolation to generate the completed first view dataset and thecompleted second view dataset cause the computer to: determine a set ofweighting values; and apply the set of weighting values to correspondingmetal dexels in the first and second view datasets.
 13. The computerreadable storage medium of claim 11 wherein the instructions that causethe computer to reconstruct an initial image cause the computer toreconstruct one of a monoenergetic image and a material decomposed imageusing the first view dataset and the second view dataset.
 14. Thecomputer readable storage medium of claim 11 wherein the instructionsthat cause the computer to reconstruct a second final image cause thecomputer to reconstruct one of a monoenergetic image and a materialdecomposed image.
 15. The computer readable storage medium of claim 11wherein the instructions cause the computer to reconstruct a secondfinal image using the completed second view dataset.
 16. The computerreadable storage medium of claim 11 wherein the instructions cause thecomputer to: modify the plurality of image voxels corresponding to themetal voxel locations of the first final image to have valuescorresponding to expected metal values for the first energy level;modify the plurality of image voxels corresponding to the metal voxellocations of the second final image to have values corresponding toexpected metal values for the second energy level; and apply a smoothingprocedure on the modified plurality of image voxels.
 17. The computerreadable storage medium of claim 11 wherein the instructions furthercause the computer to: modify the plurality of image voxelscorresponding to the metal voxel locations of the first final imageaccording to a weighted least squares fit; modify the plurality of imagevoxels corresponding to the metal voxel locations of the second finalimage according to the weighted least squares fit; scale the modifiedplurality of image voxels of the first and second final images to matchone of the metal threshold and an expected value; and apply a smoothingprocedure on the modified plurality of image voxels.
 18. The computerreadable storage medium of claim 17 wherein the instructions furthercause the computer to combine the first final image and the second finalimage to generate a monoenergetic image.
 19. A method comprising:reconstructing an image using an imaging dataset based on x-raysreceived by a detector corresponding to a first energy level; generatinga metal mask from the image corresponding to metal voxel locationswithin the imaging dataset; forward projecting the metal mask onto theimaging dataset; identifying a plurality of metal dexels and a pluralityof non-metal dexels within the imaging dataset based on the metal mask;removing the plurality of metal dexels from the imaging dataset;generating a plurality of interpolated dexels via a weightedinterpolation algorithm; replacing each of the plurality of metal dexelsin the imaging dataset with a respective interpolated dexel of theplurality of interpolated dexels; reconstructing a final image using theplurality of non-metal dexels and the plurality of interpolated dexels;and smoothing image voxels in the final image corresponding to the metalvoxel locations.
 20. The method of claim 19 wherein generating theplurality of interpolated dexels comprises generating the plurality ofinterpolated dexels using row, channel, and view data in a neighborhoodof respective metal dexels of the imaging dataset.
 21. The method ofclaim 19 further comprising: forward projecting the metal mask onto asecond imaging dataset corresponding to a second energy level differentfrom the first energy level; identifying a second plurality of metaldexels and a second plurality of non-metal dexels within the secondimaging dataset based on the metal mask; removing the second pluralityof metal dexels from the second imaging dataset; generating a secondplurality of interpolated dexels via the weighted interpolationalgorithm; replacing each of the second plurality of metal dexels in thesecond imaging dataset with a respective interpolated dexel of thesecond plurality of interpolated dexels; reconstructing a second finalimage; and smoothing image voxels in the second final imagecorresponding to the metal voxel locations.
 22. The method of claim 21further comprising: modifying the plurality of image voxelscorresponding to the metal voxel locations of the final image to havevalues corresponding to expected metal values for the first energylevel; and modifying the plurality of image voxels corresponding to themetal voxel locations of the second final image to have valuescorresponding to expected metal values for the second energy level. 23.The method of claim 19 further comprising smoothing the plurality ofinterpolated dexels using at least one neighboring dexel.
 24. The methodof claim 19 further comprising synthesizing a plurality of replacementvoxels corresponding to the metal voxels prior to forward projecting themetal mask onto the imaging dataset, wherein the plurality ofreplacement voxels correspond to one of a constant value andinterpolated values corresponding to nearby non-metal voxels.
 25. Themethod of claim 19 comprising generating the plurality of interpolateddexels using row, channel, and view data corresponding to at least oneof historical completed data and neighboring non-metal dexels.
 26. Animaging system comprising: a rotatable gantry having an opening forreceiving an object to be scanned; an x-ray source coupled to the gantryand configured to project x-rays through the opening; a generatorconfigured to energize the x-ray source to a first energy level togenerate x-rays corresponding to the first energy level; a detectorhaving pixels therein, the detector attached to the gantry andpositioned to receive x-rays projected from the x-ray source; and acomputer programmed to: access a first projection dataset correspondingto the first energy level; reconstruct a first image from the firstprojection dataset; segment the first image to identify metal locations;forward project the segmentation onto the first projection dataset toidentify a plurality of metal detector pixels in the first projectiondataset; remove the plurality of metal detector pixels from the firstprojection dataset; use a weighted interpolation to replace the removedplurality of metal detector pixels and to complete the first projectiondataset; reconstruct a completed first image using the completed firstprojection dataset; and smooth portions of the completed first imagecorresponding to the metal locations of the first energy image.
 27. Theimaging system of claim 26 wherein the computer is further programmedto: access a second projection dataset corresponding to a second energylevel; forward project the segmentation onto the second projectiondataset to identify a plurality of metal detector pixels in the secondprojection dataset; remove the plurality of metal dexels from the secondprojection dataset; use a weighted interpolation to replace the removedplurality of metal detector pixels and to complete the second projectiondataset; reconstruct a completed second image using the completed secondprojection dataset; and smooth portions of the completed second imagecorresponding to metal locations of the second projection dataset. 28.The imaging system of claim 27 wherein the computer is furtherprogrammed to: modify portions of the completed first imagecorresponding to metal locations such that the completed first imagedepicts expected metal values for the metal locations at the firstenergy level; and modify portions of the completed second imagecorresponding to metal locations such that the completed second imagedepicts expected metal values for the metal locations at the secondenergy level.
 29. The imaging system of claim 26 wherein the computer isprogrammed to apply a weighted three-dimensional interpolation using atleast one of historical completed data and row, channel, and view datato replace the removed plurality of metal detector pixels.